random.seed(args.random_seed)
model_args = args
if torch.cuda.is_available() and not args.gpuid:
print("WARNING: You have a CUDA device, should run with -gpuid 0")
if args.gpuid:
cuda.set_device(args.gpuid[0])
if args.random_seed > 0:
torch.cuda.manual_seed(args.random_seed)
loading_timer = tm.time()
schema = Schema(model_args.schema_path, None)
data_generator = get_data_generator(args, model_args, schema)
mappings = data_generator.mappings
if args.vocab_only:
import sys; sys.exit()
if args.verbose:
print("Finished loading and pre-processing data, took {:.1f} seconds".format(tm.time() - loading_timer))
# TODO: load from checkpoint
ckpt = None
# Build the model
model = build_model(model_args, args, mappings, ckpt, model_path=args.agent_checkpoint)
tally_parameters(model)
create_path(args.model_path)
config_path = os.path.join(args.model_path, 'config.json')
dummy_parser = argparse.ArgumentParser(description='duh')
add_model_arguments(dummy_parser)
add_data_generator_arguments(dummy_parser)
dummy_args = dummy_parser.parse_known_args([])[0]
if cuda.is_available() and not args.gpuid:
print("WARNING: You have a CUDA device, should run with --gpuid 0")
if args.gpuid:
cuda.set_device(args.gpuid[0])
# Load the model.
mappings, model, model_args = \
model_builder.load_test_model(args.checkpoint, args, dummy_args.__dict__)
# Figure out src and tgt vocab
make_model_mappings(model_args.model, mappings)
schema = Schema(model_args.schema_path, None)
data_generator = get_data_generator(args, model_args, schema, test=True)
# Prefix: [GO]
scorer = Scorer(args.alpha)
generator = get_generator(model, mappings['tgt_vocab'], scorer, args,
model_args)
builder = UtteranceBuilder(mappings['tgt_vocab'],
args.n_best,
has_tgt=True)
evaluator = Evaluator(model, mappings, generator, builder, gt_prefix=1)
evaluator.evaluate(args, model_args, data_generator)
